A survey of regularity, approximation, and reinforcement learning guarantees for partially observed Markov decision processes, drawing mostly on the authors' earlier work.
Unique Ergodicity of Non-Linear Filters via Reachability and Uniform Weak Continuity
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abstract
We present a reachability based approach to establish unique ergodicity of non-linear filter processes where state space of a hidden Markov model is a compact Polish metric space and the observation space is a Polish metric space. We also establish a weak convergence result on occupation measures under such a reachability condition. Our conditions, which are explicit, are complementary to those based on filter stability as demonstrated in examples.
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Partially Observed Optimal Stochastic Control: Regularity, Optimality, Approximations, and Learning
A survey of regularity, approximation, and reinforcement learning guarantees for partially observed Markov decision processes, drawing mostly on the authors' earlier work.